Magnetic resonance spectroscopy to classify tissue

ABSTRACT

Robust classification methods analyse magnetic resonance spectroscopy (MRS) data (spectra) of fine needle aspirates taken from breast tumours. The resultant data when compared with the histopathology and clinical criteria provide computerized classification-based diagnosis and prognosis with a very high degree of accuracy and reliability. Diagnostic correlation performed between the spectra and standard synoptic pathology findings contain detail regarding the pathology (malignant versus benign), vascular invasion by the primary cancer and lymph node involvement of the excised axillary lymph nodes. The classification strategy consisted of three stages: pre-processing of MR magnitude spectra to identify optimal spectral regions, cross-validated Linear Discriminant Analysis, and classification aggregation via Computerised Consensus Diagnosis. Malignant tissue was distinguished from benign lesions with an overall accuracy of 93%. From the same spectrum, lymph node involvement was predicted with an accuracy of 95% and tumour vascularisation with an overall accuracy of 92%.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority on, and incorporate by reference, U.S.Provisional Applications Ser. No. 60/160,029 filed Oct. 18, 1999.

STATEMENT REGARDING FEDERALLY SPONSERED RESEARCH DEVELOPMENT

The work described herein was supported by U.S. Army Grant number DAMD17-96-1-6077 and NH & MRC 950215 and NH & MRC 973769.

BACKGROUND OF THE INVENTION

1. Technical Field of the Invention

The present invention relates to the use of magnetic resonancespectroscopy, and more particularly to such use for determiningpathology, vascularization and nodel involvement of a biopsy of breasttissue.

2. Description of the Related Art

Within this application several publications are referenced by arabicnumerals within parentheses. Full citations for these and otherreferences may be found at the end of the specification immediatelypreceding the claims. The disclosures of all of these publications intheir entireties are hereby incorporated by reference into thisapplication in order to more fully describe the state of the art towhich this invention pertains. Clinical evaluation, mammography andaspiration cytology or core biopsy (triple assessment) is undertaken onwomen presenting with breast lesions in most Western countries. Clinicalassessment of palpable breast lumps is unreliable (1, 2). Impalpablelesions are usually discovered by screening or diagnostic mammography,which has a reported sensitivity of 77-94% and a specificity of 92-95%(3). Cytological assessment of fine needle aspiration biopsies (FNAB)has sensitivities ranging from 65-98% and specificities ranging from34-100% (4) depending on the skill of the person performing theaspiration and the expertise of the cytopathologist.

Following surgical excision of the lesion a time consuming process ofpreparation and pathological assessment of the specimen determines thenature of the tumour and the prognostic features associated with it.

SUMMARY OF THE INVENTION

Magnetic resonance spectroscopy (MRS) is a modality with a proven recordin the diagnosis of minimally invasive malignant lesions (5-11). MRspectra of small samples of tissue or even cell suspensions enable thereliable determination of whether the tissue of origin is malignant orbenign. Often MRS is able to detect malignancy before morphologicalmanifestations are visible by light microscopy (8).

The potential ofproton MRS from FNAB specimens to distinguish benignfrom malignant breast lesions has been demonstrated previously (12). Atthat time the MRS method relied on visual reading to process spectra andcalculate the ratio of the diagnostic metabolites choline and creatine.This spectral ratio allowed tissue to be identified as either benign ormalignant. In a small cohort of 20 patients within that study it alsodistinguished high grade ductal carcinoma in situ (DCIS) withcomedonecrosis or microinvasion from low grade DCIS. Despite thelimitation of visual inspection, which could only assess those spectrawith a signal to noise ratio (SNR) of greater than 10, the visual methodresulted in a diagnosis of malignant or benign with a sensitivity andspecificity of 95 and 96% . FIG. 1 shows malignant and benign spectrawith good SNR while FIG. 2 shows spectra with poor SNR.

Twenty percent of the spectra were discarded because low aspiratecellularity yielded inadequate SNR. In the initial study visual analysisused only two of fifty or more available resonances (6). Thus,potentially diagnostic and prognostic information in the remainingspectrum may have been ignored.

A 3-stage, robust statistical classification strategy (SCS) has beendeveloped to classify biomedical data and to assess the full MR spectrumobtained from biological samples. The robustness of the method has beendemonstrated previously with the analysis of proton MR spectra ofthyroid tumours (13), ovarian (14), prostate (9), and brain tissues(15). The present invention applies SCS to assess proton MR spectra ofbreast aspirates against pathological criteria in order to determine thecorrect pathology on samples with sub-optimal cellularity and SNR and todetermine if other diagnostic and prognostic information is available inthe spectra.

The inventors have determined that SCS on MRS from breast FNAB is morereliable than visual inspection to determine whether a lesion is benignor malignant, and that a greater proportion of spectra is useful foranalysis. Furthermore, spectral information obtained from MRS on FNAB ofbreast cancer specimens predicted lymph node metastases (overallaccuracy of 96% ) and vascular invasion (overall accuracy of 92% ).

The invention provides a method for obtaining a statistical classifierfor classifying spectral data from a biopsy of breast tissue todetermine the classification of a characteristic of the breast tissue,comprising:

-   -   (a) locating a plurality of maximally discriminatory subregions        in magnetic resonance spectra of biopsies of breast tissue        having known classifiers of a characteristic,    -   (b) cross-validating the spectra by selecting a portion of the        spectra, developing linear discriminant analysis classifiers        from said first portion of spectra, and validating the remainder        ofthe spectra using the classifiers from the first portion of        the spectra, to obtain optimized linear discriminant analysis        coefficients,    -   (c) repeating step (b) a plurality of times, each time selecting        a different portion of the spectra, to obtain a different set of        optimized linear discriminant analysis coefficients for each of        said plurality of times;    -   (d) obtaining a weighted average of the linear discriminant        analysis coefficients to obtain final classifier spectra        indicating the classification of the characteristic based on the        spectra; and    -   (e) comparing spectra from a biopsy of breast tissue of unknown        classification of a characteristic to the final classifier        spectra to determine the classification of the characteristic of        the breast tissue.

The invention provides an apparatus for obtaining a statisticalclassifier for classifying spectral data from a biopsy of breast tissueto determine the classification of a characteristic of the breasttissue, comprising:

-   -   (a) a locator for locating a plurality of maximally        discriminatory subregions in magnetic resonance spectra of        biopsies of breast tissue having known classifiers of a        characteristic of breast tissue,    -   (b) a cross-validator for selecting a portion of the spectra,        developing linear discriminant analysis classifiers from said        first portion of spectra, and validating the remainder of the        spectra using the classifiers from the first portion of the        spectra, to obtain optimized linear discriminant analysis        coefficients, said cross-validator selecting, developing and        validating a plurality of times, each time selecting a different        portion of the spectra, to obtain a different set of optimized        linear discriminant analysis coefficients for each of said        plurality of times, and    -   (c) an averager for obtaining a weighted average of the linear        discriminant analysis coefficients to obtain final classifier        spectra indicating the classification of the characteristic        based on the spectra,        whereby spectra from a biopsy of breast tissue of unknown        classification of a characteristic may be compared to the final        classifier spectra to determine the classification of the        characteristic of the breast tissue.

The invention provides a method for determining the classification of acharacteristic of breast tissue, comprising:

-   -   obtaining magnetic resonance spectra of a biopsy ofbreast tissue        having unknown classification of a characteristic and comparing        the spectra with a classifier, said classifier having been        obtained by:    -   (a) locating a plurality of maximally discriminatory subregions        in the magnetic resonance spectra of biopsies of breast tissue        having known classifications of a characteristic of the breast        tissue,    -   (b) cross-validating the spectra of (a) by selecting a portion        of spectra, developing linear discriminant analysis classifier        from said first portion of spectra, and validating the remainder        of the spectra using the classifications from the first portion        of the spectra, to obtain optimized linear discriminant analysis        coefficients,    -   (c) repeating step (b) a plurality of times, each time selecting        a different portion of the spectra, to obtain a different set of        optimized linear discriminant analysis coefficients for each of        said plurality of times, and    -   (d) obtaining a weighted average of the linear discriminant        analysis coefficients to obtain final classifier spectra        indicating the classification of the characteristic based on the        spectra, and        comparing the spectra from the biopsy of breast tissue having        unknown classification to the final classifier spectra to        determine the classification of the characteristic of the breast        tissue.

The invention provides an apparatus for determining the classificationof a characteristic ofbreast tissue, comprising:

-   -   a spectrometer for obtaining magnetic resonance spectra of a        biopsy of breast tissue having unknown classification of a        characteristic;    -   a classifier for statistically classifying the spectra by        comparing the spectra with a reference classifications, said        classifier having been obtained by:    -   (a) locating a plurality of maximally discriminatory subregions        in the magnetic resonance spectra of biopsies of breast tissue        having known classifications of a characteristic of the breast        tissue,    -   (b) cross-validating the spectra of (a) by selecting a portion        of spectra, developing linear discriminant analysis classifier        from said first portion of spectra, and validating the remainder        ofthe spectra using the classifiers from the first portion of        the spectra, to obtain optimized linear discriminant analysis        coefficients,    -   (c) repeating step (b) a plurality of times, each time selecting        a different portion of the spectra, to obtain a different set of        optimized linear discriminant analysis coefficients for each of        said plurality of times, and    -   (d) obtaining a weighted average of the linear discriminant        analysis coefficients to obtain final classifier spectra        indicating the classification of the characteristic based on the        spectra, and        wherein said classifier compares the spectra from the biopsy of        breast tissue having unknown classification to the final        classifier spectra to determine the classification of the        characteristic of the breast tissue.

DESCRIPTION OF THE DRAWINGS

FIG. 1 shows malignant and benign spectra with relatively good SNR;

FIG. 2 shows spectra with relatively poor SNR; and

FIG. 3 shows a system for determining pathology, vascularization andnodal involvement according to the invention.

DETAILED DESCRIPTION OF THE INVENTION

Methods

Preparation of Patients:

Intra-operative FNAB were taken from 139 patients undergoing breastsurgery for malignant and benign conditions (Table 1) by three surgeonsin separate hospitals. In order to provide a sufficiently large data setfor SCS an additional 27 patients joined the study (see Table 1).Impalpable breast lesions that had been localised by carbon track orhook wire were included except if the lesion was not palpable atexcision or when the pathology specimen could have been compromised. Allsamples were taken during surgery under direct vision after the lesionhad been identified and incised sufficiently widely to ensure that theFNAB and tissue specimens represented the same lesion and were thuscomparable. The lesion was identified and incised in-vivo via the marginwith the greatest apparent depth of normal tissue between it and thelesion to ensure the pathologist could report upon the lesion accordingto a standard protocol. Malignant and suspicious lesions were orientatedwith sutures and radio opaque vascular clips (Ligaclips) forpathological and radiological orientation. The FNAB was collected by thesurgeon using a 23-gauge needle on a 5 ml syringe. The number of needlepasses was recorded and the surgeon's evaluation of the quality of theaspirate was made. Before the needle was removed from the lesion, atissue sample including the relevant part of the needle track was taken.The size of this tissue specimen was estimated and recorded by thesurgeon.

Pre-operative clinical and investigative data included localised pain,nipple discharge or nipple crusting, details of previous mammography,and whether the lesion was detected through screening. The clinical,mammography, ultra sonographic, cytological, core biopsy and MRI detailswere recorded as malignant, suspicious, benign, impalpable, uncertain ornot done. The pathology specimen was sent on ice at the initial stages,but later in formalin, for standard histopathological reporting andhormone receptor analysis. The pathology report was issued in synopticformat (16).

Specific tumour-related clinical and sampling information was collected.These included a history of previous breast biopsies with dates,diagnoses, sizes and sites of these lesions along with the currentlesion's duration, palpability, laterality, size and locality within thebreast. The date of operation, the extent of breast surgery from openbiopsy to total mastectomy, and axillary surgery from sampling to level3 dissection was recorded.

Specimen Preparation:

Following complete excision ofthe lesions the FNAB cytology and tissuespecimens were placed in polypropylene vials containing 300 mlphosphate-buffered saline (PBS) in D₂0. All specimens were immediatelyimmersed in liquid nitrogen and stored at −70° C for up to 6 weeks untilMRS analysis.

Prior to the proton MRS experiment, each FNAB specimen was thawed andtransferred directly to a 5 mm MRS tube. The volume was adjusted to 300ml with PBS/D₂O where necessary. Proton MRS assessment of all specimenswas performed without knowledge of the correlative histopathology,either from the synoptic pathology report or from sectioning of tissueused in MRS study.

The sample of tissue excised around the needle tract was similarlyplaced in polypropylene vials containing 300 ml PBS/D₂O and immersed inliquid nitrogen as described above. This sample was later used forpathological correlation.

Data Acquisition:

MRS experiments were carried out on a Bruker Avance 360 wide-borespectrometer (operating at 8.5 Tesla) equipped with a standard 5 mmdedicated proton probehead. The sample was spun at 20 Hz and thetemperature inaintained at 37° C. The residual water signal wassuppressed by selective gated irradiation. The chemical shifts ofresonances were referenced to aqueous sodium3-(trimethylsilyl)-propanesulphonate (TSPS) at 0.00 ppm. One-dimensionalspectra were acquired over a spectral width of 3597 Hz (10.0 ppm) usinga 90° pulse of 6.5-7 μs, 8192 data points, 256 accumulations and arelaxation delay of 2.00 seconds, resulting in a pulse repetition timeof 3.14 seconds.

SNR was determined using the Bruker standard software (xwinnmr). Thenoise region was defined between 8.5 to 9.5 ppm. The signal region wasdefined between 2.8 to 3.5 ppm.

Histopathology:

Diagnostic correlation was obtained by comparing spectral analysis withthe hospital pathology report provided for each patient. Lymph nodeinvolvement and vascular invasion were determined from the reports onlyin cases where this information was complete. In the participatinghospitals lymph nodes were embedded and serial sectioned in standardfashion. One 5 μm section out of every 50 (i.e., each 250 μm) wasstained and examined. All intervening sections were discarded.

In the initial phase of the study, cytological analysis of the aspirateafter MRS analysis was attempted but cellular detail was compromised byautolytic changes and this approach was not pursued. In order to verifyFNAB sampling accuracy, a separate histopathological assessment by asingle pathologist (PR) was obtained from tissue removed from theaspiration site of the MRS sample. Tissue specimens were thawed, fixedin FAA (formalin/acetic acid/alcohol), paraffin-embedded, sectioned at 7μm, stained with haematoxylin and eosin according to standard protocolsand reviewed under the light microscope by the pathologist withoutaccess to the clinical or MRS data. Tissue preservation, abundance ofepithelial cells relative to stroma, and resence of potentiallyconfounding factors such as fat and inflammatory cells were reported inaddition to the principal diagnosis.

Statistical Classification Strategy:

The general classification strategy has been developed and was designedspecifically for MR and IR spectra ofbiofluids and biopsies. Thestrategy consists ofthree stages. First the MR magnitude spectra arepreprocessed, (in order to eliminate redundant information and/or noise)by submitting them to a powerful genetic algorithm-based Optimal RegionSelection (ORS_GA) (17), which finds a few (at most 5-10) maximallydiscriminatory subregions in the spectra. The spectral averages in thesesubregions are the ultimate features and used at the second stage. Thisstage uses the features found by ORS_GA to develop Linear DiscriminantAnalysis (LDA) classifiers that are made robust by IBD's bootstrap-basedcrossvalidation method (18). The crossvalidation approach proceeds byrandomly selecting about half the spectra from each class and usingthese to train a classifier (usually LDA). The resulting classifier isthen used to validate the remaining half. This process is repeated Btimes (with random replacement), and the optimized LDA coefficients aresaved. The weighted average of these B sets of coefficients produces thefinal classifier. The ultimate classifier is the weighted output of the500-1000 different bootstrap classifier coefficient sets and wasdesigned to be used in a clinical setting as the single best classifier.The classifier consists of probabilities of class assignment for theindividual spectra. For 2-class problems, class assignment is calledcrisp if the class probability is >0.75% . For particularly difficultclassification problems the third stage is activated. This aggregatesthe outputs (class probabilities) of several independent classifiers toform a Computerised Consensus Diagnosis (CCD) (13, 15). The consequenceof CCD is that classification accuracy and reliability is generallybetter than the best ofthe individual classifiers.

FIG. 3 shows a spectrometer 10, which may be a Bruker Avance 360spectrometer operating at 8.5 Tesla, with equipped computer. Thestatistical classification strategy (SCS) computer 12 stores the SCS andother programs described herein. The clinical data base includes theinformation from the data acquisition and histopathology, used by thecomputer 12 to develop the classifier 16. The classifier 16 classifiesthe characteristics (e.g. pathology, vascularization and/or lymph nodeinvolvement) of the breast tissue under examination.

Results

One hundred and sixty-six patients were involved in the study. A summaryof the clinicopathological criteria is shown in Table 1.

Benign Versus Malignant:

Proton MR spectra were recorded for each FNAB irrespective of thecellularity of the aspirate. However, those specimens with a SNR lessthan 10, which were shown to be inadequate for visual inspection (12)have been included in the SCS analysis without significantlycompromising accuracy. Visual inspection of all spectra irrespective ofsignal to noise gave a sensitivity and specificity of 85.3% and 81.5%respectively (Table 2a), based on the creatine-to-choline ratio. WhenSCS-based classifiers were developed for all available spectra (Table2b) , 96% of the spectra were considered crisp and could be assignedunambiguously by the classifier as malignant or benign. Sensitivity andspecificity were 93% and 92% respectively.

After removing the 31 spectra with the previously determined poor SNR(SNR<10), a sensitivity and specificity of 98% and 94% , respectively,was achieved with crispness of 99% (Table 2c).

Prognostic factors:

With the addition of prognostic criteria to the database two furtherclassifiers were created, namely, lymph node involvement and vascularinvasion. A small number of known benign or pre-invasive cases wereincluded in these subsets to assess the computer's ability to correctlydefine those cases in which no nodal involvement or vascular invasionwas expected. These benign or pre-invasive cases were all correctlyassigned by the computer into their respective uninvolved classes.

Lymph Node Involvement:

There were 31 cases with nodal involvement and 30 without including 2DCIS and 3 fibrocystic specimens. All spectra were included irrespectiveof SNR. Only those spectra for which complete pathology and clinicalreports were available were included in this comparison (Table 1). Thepresence of lymph node metastases was predicted by SCS with asensitivity of 96% and specificity of 94% (Table 3a).

Vascular Invasion:

SCS-based analysis of spectra was also carried out using vascularinvasion as the criterion. There were 85 spectra for this analysis(Table 1). A sensitivity of 84% and specificity of 100% was achieved forthe correct determination of vascular invasion, with an overall accuracyof 92% (Table 3b).

Discussion

The introduction of preprocessing and SCS analysis of MR spectra hasenhanced the ability to correlate spectroscopic changes with thepathology of human biopsies. It has also allowed specimens withsub-optimal cellularity to be analysed, and more importantly, provided acorrelation with clinical criteria not apparent by visual inspection.

Visual inspection of spectra, like histopathology, is limited by theexperience and skill of the reader for determining peak. height ratiosof metabolites (12). Visual inspection of spectra and the use of peakheight ratio measurements of choline and creatine discriminated benignfrom malignant spectra with a higher degree of accuracy than standardtriple assessment of breast lesions. However, to attain a high degree ofaccuracy, many spectra with poor SNR had to be discarded, reducing theeffectiveness of the technique. Previous estimates of cellular materialderived from FNAB, on which to perform MRS analysis reliably, havesuggested that at least 10₆ cells are needed (6).

By using SCS-derived classifiers it was possible to distinguishmalignant from benign pathologies with higher sensitivity 92% andspecificity 96% for all FNAB spectra including those with low SNR (Table2b) than by visual reading of these same spectra (Table 2a). ThatSCS-based analysis could more reliably classify a greater proportion ofspectra than could be visually assessed is testament to the robustnessand greater generality of the computer-based approach.

The SCS-based result is further improved by presenting to the computerspectral data with high SNR. The improvement in sensitivity andspecificity gained for spectra with SNR>10 (Table 2c) illustrates thispoint. Obtaining FNAB with adequate cell numbers can also enhance theresults. SCS permits classifiers to recognise patterns containing morecomplex information. The classifier has been validated to diagnosespecimens with lymph node involvement and vascular invasion. The abilityof the SCS-derived classifier to predict lymph node involvement with anaccuracy of 95% and vascular invasion with an accuracy of 92% emphasisesthe wealth of chemical information that can be extracted, with theappropriate statistical approach, from an FNAB of a breast lesion (Table3).

A major challenge in breast cancer is the need to identify andunderstand the factors that most influence the patient's prognosis andthrough timely and appropriate intervention influence this outcome.Adjuvant therapy can reduce the odds of death during the first ten yearsafter diagnosis of breast cancer by about of 20-30% (19). The bestprognostic indicator of survival in patients with early breast cancerhas been shown to be axillary lymph node status (20-22).

Increasingly, sentinel lymph node biopsy is being investigated as ameans to reduce the morbidity and cost of unnecessary axillarydissection in the two thirds of women with early invasive breast cancerwho prove to be node-negative (23-25), while preserving the option offull axillary node clearance in those patients who are node-positive.MRS may possibly determine nodal involvement from the cellular materialderived solely from the primary tumour, thus limiting the role ofsentinel lymph node biopsy.

The results, that 52% of patients with lymph node involvement also hadvascular invasion, is in agreement with Barth et al (26), who showedthat peritumoural lympho-vascular invasion correlated with lymph nodeinvolvement (27) and was an independent predictor of disease free andoverall survival (28-31).

A computer-based statistical classification strategy providing a robustmeans of analysing clinical data is becoming a reality. The power, speedand reproducibility of a computer-based diagnosis may lead to suitablyprogrammed computers supplanting the human observer in the clinicallaboratory. Patients increasingly expect certainty in diagnosis andoptimum management.

Several important experimental factors should be noted. Presently, theMRS method according to the invention has thus far been demonstrated towork only on aspirated cells from the breast and not on core biopsiesthat contain a sufficiently high level of fat to mask diagnostic andprognostic information. The biopsy should be representative of thelesion and contain sufficient cellularity. Furthermore, sample handlingis of paramount importance if the specimen is to be minimally degraded.Quality control in the spectrometer should be exercised with regard topulse sequences, temperature, magnet stability, shimming and watersuppression. The magnetic field at which the database reported hereinwas collected is 8.5 Tesla (360 MHz for proton). Because spectralpatterns are frequency dependent, a new classifier should be developedif one uses different magnetic field strengths.

The clinical and pathology databases used to train the classifier shouldbe representative of the full range of pathologies or the completedemographics of the population, or else the classifier may beinadequately prepared for all the possibilities it might encounter inclinical practice. In developing a database for breast lesions, thetraining set should have adequate samples of all the commonlyencountered breast pathologies and be updated upon detection of lesscommon tumour types.

The invention is expected to provide a revolutionary impact on breastcancer management by the use of SCS computerised analysis of MR spectralfeatures, by obtaining a much higher level of accuracy in diagnosis ofthe lesion and also an indication of its metastatic potential whencompared to visual inspection of spectra. Most importantly, theinvention facilitates identification of the stage of the disease fromspectral information of FNAB collected only from the primary breastlesion.

The invention allows one to determine pathological diagnosis, thelikelihood of axillary lymph nodal involvement and tumourvascularisation by SCS-based analysis of proton MR spectra of a FNABtaken from a primary breast lesion. The SCS-based method is moreaccurate and reliable than visual inspection for identifying complexspectral indicators of diagnosis and prognosis.

The ability of an SCS-based analysis of MRS data to provide prognosticinformation on lymph node involvement by sampling only the primarytumour may provide a paradigm shift in the management of breast cancer.The determination of vascular invasion from the same cellular materialhighlights the untapped potential of MRS to determine prognosticinformation.

Although one embodiment of the invention has been shown and described,numerous variations and modifications will readily occur to thoseskilled in the art. The invention is not limited to the preferredembodiment, and its scope is determined only by the appended claims.TABLE 1 Summary of Clinico-pathological Data All patientsBenign/Malignant Lymph Nodes Vascular Invasion (n = 66) (n = 140) (n =61) (n = 85) Age Mean ± SD (Range) 55.8 ± 15.4 (20-101) 54.7 ± 15(20-90) 58.4 ± 13.2 (29-85) 60.6 ± 14.3 (29-101) Pathology type InvasiveDuctal 89 74 52 68 Invasive Lobular 8 8  3  5 Mixed Ductal/Lob. 1  1  1DCIS 10 1  2*  9* Fibroadenoma 17 17 Fibrocystic 22 22  3*  2* Papilloma3 2 Radial Scar 2 2 Gynaecomastia 1 1 Misc. Benign 13 12  1 Total 166140 61 85*These preinvasive and benign lesions were included as known lymph nodenegative, vascular invasion negative cases to test the computer'sability to discern true negatives and positives. They were all correctlyclassified by the computer into their respective classes.

TABLE 2 Maglignant versus Benign a. Visual Inspection: Malignant versusBenign Sensitivity Specificity For all Spectra Malignant (n = 83)vsBenign (n = 57) 85.3% 81.5% Spectra SNR > 10 Malignant (n = 60)vs Benign(n = 49)  100% 87.3% b. SCS: Malignant or Benign (All spectra): (M: 83,B: 57) B M Sensitivity Specificity PPV % Crisp B 51  4 92.7% 92.4% 92.4%96.5% M  6 73 92.4% 92.7% 92.7% 95.2% Overall Accuracy: 92.6% Overall %Crisp: 95.7% (134 of 140) x = 0.922 c. SCS: Malignant or Benign (SNR >10): (M: 60, B: 49) B M Sensitivity Specificity PPV % Crisp B 46  393.9% 98.3% 98.2% 100.0% M  1 58 98.3% 93.9% 94.1%  98.3% OverallAccuracy: 96.1% Overall % crisp: 99.1% (108 of 109) x = 0.922

TABLE 3 SCS:-Prognostic Indicators a. Lymph Node involvement: (P:(Present) 29, A: (Absent) 32) P A Sensitivity Specificity PPV % Crisp P25  1 96.2% 93.8% 93.9% 89.7% A  2 30 93.8% 96.2% 96.1%  100% OverallAccuracy: 95.0% Overall % crisp: 95.1% (58 of 61) x = 0.899 b. VascularInvasion: (P: (Present) 33, A: (Absent) 52) P A Sensitivity SpecificityPPV % Crisp P 26  5  83.9% 100.0% 100.0% 93.9% A  0 49 100.0%  83.9% 86.1% 94.2% Overall Accuracy: 91.9% Overall % crisp: 94.1% (80 of 85) x= 0.839REFERENCES

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1-26. (canceled)
 27. a method for obtaining a statistical classifier forclassifying spectral data from a biopsy of tissue to determine theclassification of a characteristic of the tissue, comprising: (a)locating a plurality of discriminatory subregions in magnetic resonancespectra of biopsies of tissue having known classifiers of acharacteristic, (b) cross-validating the spectra by selecting a firstportion of the spectra comprising about one-half of the spectra leavingthe other one-half of the spectra in the remaining portion of thespectra, developing linear discriminant analysis classifiers from saidfirst portion of spectra, and validating the remainder of the spectrausing the classifiers from the first portion of the spectra, to obtainoptimized linear discriminant analysis coefficients, (c) repeating step(b) a plurality of times, each time selecting a different portion of thespectra, to obtain a different set of optimized linear discriminantanalysis coefficients for each of said plurality of times; and (d)obtaining an average of the linear discriminant analysis coefficients toobtain final classifier spectra indicating the classification of thecharacteristic based on the spectra, wherein spectra from a biopsy oftissue of unknown classification of a characteristic may be compared tothe final classifier spectra to determine the classification of thecharacteristic of the tissue.
 28. An apparatus for obtaining astatistical classifier for classifying spectral data from a biopsy oftissue to determine the classification of a characteristic of thetissue, comprising: (a) a locator for locating a plurality ofdiscriminatory subregions in magnetic resonance spectra of biopsies oftissue having known classifiers of a characteristic of tissue, (b) across-validator for selecting a first portion of the spectra comprisingabout one-half of the spectra leaving the other one-half of the spectrain the remaining portion of the spectra, developing linear discriminantanalysis classifiers from said first portion of spectra, and validatingthe remainder of the spectra using the classifiers from the firstportion of the spectra, to obtain optimized linear discriminant analysiscoefficients, said cross-validator selecting, developing and validatinga plurality of times, each time selecting a different portion of thespectra, to obtain a different set of optimized linear discriminantanalysis coefficients for each of said plurality of times, and (c) anaverager for obtaining an average of the linear discriminant analysiscoefficients to obtain final classifier spectra indicating theclassification of the characteristic based on the spectra, wherebyspectra from a biopsy of tissue of unknown classification of acharacteristic may be compared to the final classifier spectra todetermine the classification of the characteristic of the tissue.
 29. Amethod for determining the classification of a characteristic of tissue,comprising: obtaining magnetic resonance spectra of a biopsy of tissuehaving unknown classification of a characteristic and comparing thespectra with a classifier, said classifier having been obtained by: (a)locating a plurality of discriminatory subregions in the magneticresonance spectra of biopsies of tissue having known classifications ofa characteristic of the tissue, (b) cross-validating the spectra of (a)by selecting a first portion of spectra comprising about one-half of thespectra leaving the other one-half of the spectra in the remainder ofthe spectra, developing linear discriminant analysis classifier fromsaid first portion of spectra, and validating the remainder of thespectra using the classifications from the first portion of the spectra,to obtain optimized linear discriminant analysis coefficients, (c)repeating step (b) a plurality of times, each time selecting a differentportion of the spectra, to obtain a different set of optimized lineardiscriminant analysis coefficients for each of said plurality of times,and (d) obtaining a weighted average of the linear discriminant analysiscoefficients to obtain final classifier spectra indicating theclassification of the characteristic based on the spectra, wherein thespectra from the biopsy of tissue having unknown classification may becompared to the final classifier spectra to determine the classificationof the characteristic of the tissue.
 30. An apparatus for determiningthe classification of a characteristic of tissue, comprising: aspectrometer for obtaining magnetic resonance spectra of a biopsy oftissue having unknown classification of a characteristic; a classifierfor statistically classifying the spectra by comparing the spectra witha reference classifications, said classifier having been obtained by:(a) locating a plurality of discriminatory subregions in the magneticresonance spectra of biopsies of tissue having known classifications ofa characteristic of the tissue, (b) cross-validating the spectra of (a)by selecting a first portion of spectra comprising about one-half of thespectra leaving the other one-half of the spectra in the remainder ofthe spectra, developing linear discriminant analysis classifier fromsaid first portion of spectra, and validating the remainder of thespectra using the classifiers from the first portion of the spectra, toobtain optimized linear discriminant analysis coefficients, (c)repeating step (b) a plurality of times, each time selecting a differentportion of the spectra, to obtain a different set of optimized lineardiscriminant analysis coefficients for each of said plurality of times,and (d) obtaining an average of the linear discriminant analysiscoefficients to obtain final classifier spectra indicating theclassification of the characteristic based on the spectra, and whereinsaid classifier compares the spectra from the biopsy of tissue havingunknown classification to the final classifier spectra to determine theclassification of the characteristic of the tissue.
 31. A method forobtaining a statistical classifier for classifying spectral data from abiopsy of tissue to determine the classification of a characteristic ofthe tissue, comprising: (a) locating a plurality of discriminatorysubregions in magnetic resonance spectra of biopsies of tissue havingknown classifiers of a characteristic, (b) cross-validating the spectraby selecting a first portion of the spectra comprising a first pluralityof the spectra leaving the remainder of the spectra, developing lineardiscriminant analysis classifiers from said first portion of spectra,and validating the remainder of the spectra using the classifiers fromthe first portion of the spectra, to obtain optimized lineardiscriminant analysis coefficients, (c) repeating step (b) a pluralityof times, each time selecting a different portion of the spectra to formthe first portion, to obtain a different set of optimized lineardiscriminant analysis coefficients for each of said plurality of times;and (d) obtaining an average of the linear discriminant analysiscoefficients to obtain final classifier spectra indicating theclassification of the characteristic based on the spectra, whereinspectra from a biopsy of tissue of unknown classification of acharacteristic may be compared to the final classifier spectra todetermine the classification of the characteristic of the tissue.
 32. Anapparatus for obtaining a statistical classifier for classifyingspectral data from a biopsy of tissue to determine the classification ofa characteristic of the tissue, comprising: (a) a locator for locating aplurality of discriminatory subregions in magnetic resonance spectra ofbiopsies of tissue having known classifiers of a characteristic oftissue, (b) a cross-validator for selecting a first portion of thespectra comprising a first plurality of the spectra leaving theremainder of the spectra, developing linear discriminant analysisclassifiers from said first portion of spectra, and validating theremainder of the spectra using the classifiers from the first portion ofthe spectra to fonn the first portion, to obtain optimized lineardiscriminant analysis coefficients, said cross-validator selecting,developing and validating a plurality of times, each time selecting adifferent portion of the spectra, to obtain a different set of optimizedlinear discriminant analysis coefficients for each of said plurality oftimes, and (c) an averager for obtaining an average of the lineardiscriminant analysis coefficients to obtain final classifier spectraindicating the classification of the characteristic based on thespectra, whereby spectra from a biopsy of tissue of unknownclassification of a characteristic may be compared to the finalclassifier spectra to determine the classification of the characteristicof the tissue.
 33. A method for detenmining the classification of acharacteristic of tissue, comprising: obtaining magnetic resonancespectra of a biopsy of tissue having unknown classification of acharacteristic and comparing the spectra with a classifier, saidclassifier having been obtained by: (a) locating a plurality ofdiscriminatory subregions in the magnetic resonance spectra of biopsiesof tissue having known classifications of a characteristic of thetissue, (b) cross-validating the spectra of (a) by selecting a firstportion of spectra comprising a first plurality of the spectra leavingthe remainder of the spectra, developing linear discriminant analysisclassifier from said first portion of spectra, and validating theremainder of the spectra using the classifications from the firstportion of the spectra, to obtain optimized linear discriminant analysiscoefficients, (c) repeating step (b) a plurality of times, each timeselecting a different portion of the spectra to form the first portion,to obtain a different set of optimized linear discriminant analysiscoefficients for each of said plurality of times, and (d) obtaining aweighted average of the linear discriminant analysis coefficients toobtain final classifier spectra indicating the classification of thecharacteristic based on the spectra, wherein the spectra from the biopsyof tissue having unknown classification may be compared to the finalclassifier spectra to determine the classification of the characteristicof the tissue.
 34. An apparatus for determining the classification of acharacteristic of tissue, comprising: a spectrometer for obtainingmagnetic resonance spectra of a biopsy of tissue having unknownclassification of a characteristic; a classifier for statisticallyclassifying the spectra by comparing the spectra with a referenceclassifications, said classifier having been obtained by: (a) locating aplurality of discriminatory subregions in the magnetic resonance spectraof biopsies of tissue having known classifications of a characteristicof the tissue, (b) cross-validating the spectra of (a) by selecting afirst portion of spectra comprising a first plurality of the spectraleaving the remainder of the spectra, developing linear discriminantanalysis classifier from said first portion of spectra, and validatingthe remainder of the spectra using the classifiers from the firstportion of the spectra, to obtain optimized linear discriminant analysiscoefficients, (c) repeating step (b) a plurality of times, each timeselecting a different portion of the spectra to form the first portion,to obtain a different set of optimized linear discriminant analysiscoefficients for each of said plurality of times, and (d) obtaining anaverage of the linear discriminant analysis coefficients to obtain finalclassifier spectra indicating the classification of the characteristicbased on the spectra, and wherein said classifier compares the spectrafrom the biopsy of tissue having unknown classification to the finalclassifier spectra to determine the classification of the characteristicof the tissue.